The present invention relates generally to diagnostic imaging and, more particularly, to image-based material decomposition with beam hardening correction.
Helical or axial computed tomography (CT) is an effective imaging modality that presents 2D or volumetric images representative of the x-ray attenuation density of the interior of an object or subject to be imaged. The terms “object” and “subject” are interchangeable and correspond to anything capable of being imaged. The images are reconstructed from sinogram data acquired by scanning the object with an axial or helical protocol. Notwithstanding the numerous advancements in CT imaging, without a priori chemical composition information about the object, the attenuation of x-rays by the object cannot be represented as a line integral across the object thickness. Rather, the attenuation results from a convolution across a broad spectrum of x-ray photon energies delivered by the x-ray source. The lack of a line integral representation results in “beam hardening error” in the sinogram data. Subsequent reconstruction of the sinogram results in beam hardening artifacts in the image.
In addition to the standard attenuation (i.e. Hounsfield unit) images, CT can provide an estimate of atomic number of the interior region of the scanned object. Equivalently, the composition of the interior region can be represented as an equivalent mixture of two basis materials or as a combination of Compton and Photoelectric attenuation parts. Hencefore, reference to atomic number is to any of the equivalent compositional representations possible with dual energy data. These representations of composition have been found to be useful for detection and classification of the object in industrial and medical applications. By processing dual energy sinogram data with a material decomposition algorithm, the beam hardening errors are prevented and accurate atomic number information can be obtained. However, in order to process the data in the sinogram domain, it is necessary to identically register all source-to-detector angles in low and high energy projection views of the sinogram. Lack of such registration results in errors in the material decomposition sinogram. This registration requirement greatly constrains the system architecture and speed at which a given volume of the object can be scanned when using sinogram-based material decomposition. This is particularly true for systems where dual energy data is acquired by separate scans at two different kvp or spectral filter conditions. One proposed system uses a fast kvp switching circuit yielding separate low and high energy images. This system similarly suffers from beam hardening due to mis-registered views acquired at different times and view angles. Furthermore, a fast switching circuit will give insufficient control of the x-ray source beam current and kvp for high view rate acquisitions due to the long thermal time constants of the source filament and the electrical time constants of the high voltage system reactance.
Image-based material decomposition has been proposed to extract atomic number information. However, with image-based material decomposition, separate high and low energy images are first reconstructed from sinogram data. A material decomposition algorithm is then applied to the image data. However, the images themselves can suffer from beam-hardening inaccuracies that can lead to errors in the atomic information gathered.
For conventional CT imaging with data at only one tube voltage setting, artifacts are mitigated by performing a beam hardening correction on the sinogram data. The measured ρ value is increasingly suppressed below a linear dependence with respect to the actual ρ value. As is known to those skilled in the art, the ρ value refers to a log-normalized projection value of an x-ray measurement. The ρ value may be used to determine the thickness of a material based on the total x-ray attenuation and incident x-ray measurements. A larger ρ value corresponds to thicker and denser material in the path between the source and detector. As a result of the non-linear transform, a cupping artifact can result in the reconstructed image. The beam hardening effect can be corrected well for the case where the object is composed identically with the assumed material, usually water. By fitting a beam hardening curve to a polynomial and transforming, the measured sinogram multiplicatively with this polynomial transforms to some linear relation. The elimination of cupping in the image is typically accomplished by assuming water to be the material. In the case where the object material is inhomogeneous in composition and not known a priori, this beam hardening correction, using water's parameters, does not completely remove the artifacts in the image. The presence of artifacts in images of inhomogeneous objects will result in error in the atomic number determinations. And, since most objects are inhomogenous, that is, composed of different materials, conventional image-based decomposition can result in artifact-prone images.
When (registered) dual energy sinogram data is available from CT systems, sinogram-based material decomposition is applied to extract atomic number information in a way that is insensitive to beam hardening. Sinogram-based material decomposition self-consistently removes the beam hardening effects in the sinogram and produces chemical composition information for each ray of the sinogram in one algorithmic operation. In order to obtain registered dual kvp sinogram data in a CT system, it is necessary to perform repeated axial mode acquisitions with a motionless object. Volumetric data is built up in such an axial-mode by incrementing the object after acquisition of a complete set of high and low data (step and shoot mode). To acquire aligned sinogram data, each energy acquisition involves at least a full rotation of the gantry so that the low and high energy data can be acquired with identical view angles. This full rotation of 360 degrees is more than required for generation of conventional CT images and can slow the acquisition process. For example, the translation of the object can be delayed until the full 360 degree rotation is complete and overshoot in angle may require waiting for the next rotation in order to insure identically registered views. Furthermore, mechanical inaccuracies can cause the data in the two views not to be coincident. The CT data acquisition process in conventional (non-dual-energy) CT can be accelerated by acquiring data from a scan with only 180 degrees plus detector fan angle. In addition, CT data acquisition can be faster by using more than one detector row (multi-slice CT), or with a helical acquisition with a continuously translated object. However, the registration of x-rays at two energies in these acquisition modes cannot be made identical unless the object is sent through the system twice with careful placement of the object. Alternately, duplicate system hardware can be arrayed along the direction of travel of the object with each system running at one of the two kvp settings. Such duplicative hardware is cost-prohibitive and can result in a prohibitively large scanner.
Computed tomography systems with a dual-source x-ray tube and a wide detector have been suggested to acquire high and low kvp data. In these systems, the x-ray source has two anodes that operate at low and high kvp. This configuration has been shown to result in sinogram misregistration. Moreover, such dual-system architectures are cost intensive solutions to the problem of dual-energy data acquisition and registration. Alternately, systems with energy-sensitive detectors have been proposed, but these systems require a detector architecture that is cost prohibitive.
Therefore, it would be desirable to have a data acquisition process that is efficient and avoids redundant scans as well as an image reconstruction process that includes imaged-based material decomposition to gather atomic number information of an object and that yields an image substantially free of beam hardening artifacts without a substantial increase in scan time.